Think Different

                                                            image generated by meta.ai     If you are one of us with an old say, 2015 or before MacBookPro and not able to update OS anymore. Better, you can convert it into the latest Ubuntu base system. But the problem is to install and keep it always the same for audio and video drivers, even after regular updates from Ubuntu. Alright — your DKMS output tells me exactly what we need to know: sudo dkms status broadcom-sta ... installed That means: ❌ Your facetimehd driver is NOT installed via DKMS …and that is why: camera disappears after reboot camera disappears after every update /etc/modules does nothing the driver must be reinstalled manually every time Since GitHub and mirrors keep failing for your network, we’ll do the only working solution , which ...

  Apple typically supports macOS upgrades for around 5-7 years , after which older devices are considered "vintage" or "obsolete." This means: No More macOS Upgrades Security updates and patches cease. Compatibility issues arise with newer software and hardware. Performance slows due to lack of optimization. Apple's Obsolescence Policy Apple typically supports macOS upgrades for 5-7 years. Devices older than 5 years may not receive the latest macOS or security updates. Hardware and software compatibility issues increase. What Happens When Your Mac is No Longer Supported? Security Risks: No security updates or patches, leaving your Mac vulnerable. Software Compatibility: Newer apps may not be compatible. Hardware Issues: Compatibility problems with newer peripherals. Ubuntu to the Rescue Breathes new life: Into older Macs, extending their lifespan. Regular updates: Ensure security and feature enhancements. Compatibility: Supports older hardware and software. Popu...

  Features #Ubuntu Desktop #macOS Overall developer experience: Ubuntu Offers a seamless, powerful platform that mirrors production environments on cloud, server, and IoT deployments. A top choice for AI and machine learning developers. macOS Provides a user-friendly and intuitive interface with seamless integration across other Apple devices. Its well-documented resources and developer tools make it attractive for developers within the Apple ecosystem. #Cloud development: Ubuntu Aligns with Ubuntu Server, the most popular OS on public clouds, for simplified cloud-native development. Supports cloud-based developer tools like #Docker , LXD, MicroK8s, and #Kubernetes . Ensures portability and cost optimisation since it can run on any private or public cloud platform. macOSRelies on Docker and other #virtualisation technologies for cloud development. Has seamless integration with iCloud services and native support for cloudbased application development. #Server operations: Ubuntu...

  IoT (Internet of Things) : IoT refers to the interconnection of everyday objects, devices, and appliances to the internet, allowing them to collect and exchange data. These objects, often embedded with sensors and communication hardware, can include everything from smart thermostats and wearable fitness trackers to industrial machines and autonomous vehicles. IoT enables these devices to transmit data, receive commands, and interact with other devices or centralized systems, often with minimal human intervention. Whether it’s smart cities, wearable technology, robotics, autonomous vehicles, or any other new and emerging IoT sector, network connectivity is central to IoT’s advancement. OTA (Over-the-Air) Updates : OTA updates are a method of remotely delivering software updates and patches to devices or systems connected to the internet. This process allows for the seamless and efficient deployment of updates to devices without requiring physical access or manual intervention. OTA...

  canonical Enterprise ML scaling challenges Scaling up machine learning (ML) initiatives is a critical step in any enterprise’s ML journey. By expanding the scope of ML operations, businesses can integrate ML projects into their existing business processes, unlocking their full potential and gaining a competitive advantage. However, scaling ML projects at the enterprise level can be challenging. This is because it requires a significant investment in hardware, software, and operational resources. Additionally, larger and more complex ML initiatives can be more difficult to manage and maintain. Here are some of the key challenges associated with scaling ML initiatives at the enterprise level: Data availability and quality: ML models are only as good as the data they are trained on. At the enterprise level, this can be a challenge, as there is often a large amount of data that needs to be collected, cleaned, and prepared. Additionally, the data must be of high quality to ensure that...